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Tidied up code and docs for analyze().
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@LTLA
LTLA committed Jan 5, 2025
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75 changes: 42 additions & 33 deletions src/scranpy/analyze.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,26 +80,23 @@ class AnalyzeResults:
If CRISPR data is not available, this is set to ``None`` instead."""

rna_size_factors: Optional[numpy.ndarray]
"""Size factors for the RNA count matrix, derived from the sum of counts for each cell.
Size factors are centered with :py:func:`~scranpy.center_size_factors.center_size_factors`.
"""Size factors for the RNA count matrix, derived from the sum of counts for each cell and centered with :py:func:`~scranpy.center_size_factors.center_size_factors`.
If RNA data is not available, this is set to ``None`` instead."""

rna_normalized: Optional[delayedarray.DelayedArray]
"""Matrix of (log-)normalized expression values derived from RNA counts, as computed by :py:func:`~scranpy.normalize_counts.normalize_counts` using :py:attr:`~rna_size_factors`.
If RNA data is not available, this is set to ``None`` instead."""

adt_size_factors: Optional[numpy.ndarray]
"""Size factors for the ADT count matrix, computed using the CLRm1 method.
Size factors are centered with :py:func:`~scranpy.center_size_factors.center_size_factors`.
"""Size factors for the ADT count matrix, computed by :py:func:`~scranpy.compute_clrm1_factors.compute_clrm1_factors` and centered with :py:func:`~scranpy.center_size_factors.center_size_factors`.
If ADT data is not available, this is set to ``None`` instead."""

adt_normalized: Optional[delayedarray.DelayedArray]
"""Matrix of (log-)normalized expression values derived from ADT counts, as computed by :py:func:`~scranpy.normalize_counts.normalize_counts` using :py:attr:`~adt_size_factors`.
If ADT data is not available, this is set to ``None`` instead."""

crispr_size_factors: Optional[numpy.ndarray]
"""Size factors for the CRISPR count matrix, derived from the sum of counts for each cell.
Size factors are centered with :py:func:`~scranpy.center_size_factors.center_size_factors`.
"""Size factors for the CRISPR count matrix, derived from the sum of counts for each cell and centered with :py:func:`~scranpy.center_size_factors.center_size_factors`.
If CRISPR data is not available, this is set to ``None`` instead."""

crispr_normalized: Optional[delayedarray.DelayedArray]
Expand All @@ -115,15 +112,15 @@ class AnalyzeResults:
If RNA data is not available, this is set to ``None`` instead."""

rna_pca: Optional[RunPcaResults]
"""Results of calling :py:func:`~scranpy.run_pca.run_pca` on the RNA log-expression matrix.
"""Results of calling :py:func:`~scranpy.run_pca.run_pca` on :py:attr:`~rna_normalized` using the :py:attr:`~rna_highly_variable_genes` subset.
If RNA data is not available, this is set to ``None`` instead."""

adt_pca: Optional[RunPcaResults]
"""Results of calling :py:func:`~scranpy.run_pca.run_pca` on the ADT log-expression matrix.
"""Results of calling :py:func:`~scranpy.run_pca.run_pca` on :py:attr:`~adt_normalized`.
If ADT data is not available, this is set to ``None`` instead."""

crispr_pca: Optional[RunPcaResults]
"""Results of calling :py:func:`~scranpy.run_pca.run_pca` on the CRISPR log-expression matrix.
"""Results of calling :py:func:`~scranpy.run_pca.run_pca` on :py:attr:`~crispr_normalized`.
If CRISPR data is not available, this is set to ``None`` instead."""

combined_pca: Union[Literal["rna_pca", "adt_pca", "crispr_pca"], ScaleByNeighborsResults]
Expand All @@ -135,7 +132,7 @@ class AnalyzeResults:
This is set to ``None`` if no blocking factor was supplied."""

mnn_corrected: Optional[CorrectMnnResults]
"""Results of :py:func:`~scranpy.correct_mnn.correct_mnn`.
"""Results of :py:func:`~scranpy.correct_mnn.correct_mnn` on the PCs in or referenced by :py:attr:`~combined_pca`.
If no blocking factor is supplied, this is set to ``None`` instead."""

tsne: Optional[numpy.ndarray]
Expand Down Expand Up @@ -164,17 +161,17 @@ class AnalyzeResults:
If no suitable clusterings are available, this is set to ``None``."""

rna_markers: Optional[RunPcaResults]
"""Results of calling :py:func:`~scranpy.score_markers.score_markers` on the RNA log-expression matrix.
"""Results of calling :py:func:`~scranpy.score_markers.score_markers` on :py:attr:`~rna_normalized`.
If RNA data is not available, this is set to ``None`` instead.
This will also be ``None`` if no suitable clusterings are available."""

adt_markers: Optional[RunPcaResults]
"""Results of calling :py:func:`~scranpy.score_markers.score_markers` on the ADT log-expression matrix.
"""Results of calling :py:func:`~scranpy.score_markers.score_markers` on :py:attr:`~adt_normalized`.
If ADT data is not available, this is set to ``None`` instead.
This will also be ``None`` if no suitable clusterings are available."""

crispr_markers: Optional[RunPcaResults]
"""Results of calling :py:func:`~scranpy.score_markers.score_markers` on the CRISPR log-expression matrix.
"""Results of calling :py:func:`~scranpy.score_markers.score_markers` on :py:attr:`~crispr_normalized`.
If CRISPR data is not available, this is set to ``None`` instead.
This will also be ``None`` if no suitable clusterings are available."""

Expand Down Expand Up @@ -210,11 +207,13 @@ def to_singlecellexperiment(
flatten_qc_subsets:
Whether to flatten QC feature subsets,
see the :py:meth:`~scranpy.rna_quality_control.ComputeRnaQcMetricsResults.to_biocframe` method of the :py:class:`~scranpy.rna_quality_control.ComputeRnaQcMetricsResults` class for more details.
see the :py:meth:`~scranpy.rna_quality_control.ComputeRnaQcMetricsResults.to_biocframe` method
of the :py:class:`~scranpy.rna_quality_control.ComputeRnaQcMetricsResults` class for more details.
include_per_block_variances:
Whether to compute the per-block variances,
see the :py:meth:`~scranpy.model_gene_variances.ModelGeneVariancesResults.to_biocframe` method of the :py:class:`~scranpy.model_gene_variances.ModelGeneVariancesResults` class for more details.
see the :py:meth:`~scranpy.model_gene_variances.ModelGeneVariancesResults.to_biocframe` method
of the :py:class:`~scranpy.model_gene_variances.ModelGeneVariancesResults` class for more details.
Returns:
A :py:class:`~singlecellexperiment.SingleCellExperiment.SingleCellExperiment` containing the filtered and normalized matrices in the assays.
Expand Down Expand Up @@ -361,23 +360,23 @@ def analyze(
A matrix-like object containing RNA counts.
This should have the same number of columns as the other ``*_x`` arguments.
Alternatively, a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` class containing such a matrix in its ``rna_assay``.
Alternatively, a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` object containing such a matrix in its ``rna_assay``.
Alternatively ``None``, if no RNA counts are available.
adt_x:
A matrix-like object containing ADT counts.
This should have the same number of columns as the other ``*_x`` arguments.
Alternatively, a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` class containing such a matrix in its ``adt_assay``.
Alternatively, a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` object containing such a matrix in its ``adt_assay``.
Alternatively ``None``, if no ADT counts are available.
crispr_x:
A matrix-like object containing CRISPR counts.
This should have the same number of columns as the other ``*_x`` arguments.
Alternatively, a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` class containing such a matrix in its ``crispr_assay``.
Alternatively, a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment` object containing such a matrix in its ``crispr_assay``.
Alternatively ``None``, if no CRISPR counts are available.
Expand Down Expand Up @@ -411,44 +410,50 @@ def analyze(
compute_clrm1_factors_options:
Arguments to pass to :py:func:`~scranpy.compute_clrm1_factors.compute_clrm1_factors`.
Only used if \code{adt.x} is provided.
normalized_counts_options:
normalize_counts_options:
Arguments to pass to :py:func:`~scranpy.normalize_counts.normalize_counts`.
model_gene_variances_options:
Arguments to pass to :py:func:`~scranpy.model_gene_variances.model_gene_variances`.
Only used if \code{rna.x} is provided.
choose_highly_variable_genes_options:
Arguments to pass to :py:func:`~scranpy.choose_highly_variable_genes.choose_highly_variable_genes`.
Only used if \code{rna.x} is provided.
run_pca_options:
Arguments to pass to :py:func:`~scranpy.run_pca.run_pca`.
use_rna_pcs:
Whether to use the RNA-derived PCs for downstream steps (i.e., clustering, visualization).
Only used if \code{rna.x} is provided.
use_adt_pcs:
Whether to use the ADT-derived PCs for downstream steps (i.e., clustering, visualization).
Only used if \code{adt.x} is provided.
use_crispr_pcs:
Whether to use the CRISPR-derived PCs for downstream steps (i.e., clustering, visualization).
Only used if \code{crispr.x} is provided.
scale_by_neighbors_options:
Arguments to pass to :py:func:`~scranpy.scale_by_neighbors.scale_by_neighbors`.
Only used if multiple modalities are available and their corresponding ``use_*`` arguments are ``True``.
Only used if multiple modalities are available and their corresponding ``use_*_pca`` arguments are ``True``.
correct_mnn_options:
Arguments to pass to :py:func:`~scranpy.correct_mnn.correct_mnn`.
Only used if ``block`` is supplied.
run_umap_options:
Arguments to pass to :py:func:`~scranpy.run_umap.run_umap`.
If ``None``, UMAP is not performed.
run_tsne_options:
Arguments to pass to :py:func:`~scranpy.run_tsne.run_tsne`.
If ``None``, t-SNE is not performed.
run_umap_options:
Arguments to pass to :py:func:`~scranpy.run_umap.run_umap`.
If ``None``, UMAP is not performed.
build_snn_graph_options:
Arguments to pass to :py:func:`~scranpy.build_snn_graph.build_snn_graph`.
Ignored if ``cluster_graph_options = None``.
Expand Down Expand Up @@ -480,6 +485,13 @@ def analyze(
Algorithm to use for nearest-neighbor searches in the various steps.
rna_assay:
Integer or string specifying the assay to use if ``rna_x`` is a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment`.
adt_assay:
Integer or string specifying the assay to use if ``adt_x`` is a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment`.
crispr_assay:
Integer or string specifying the assay to use if ``crispr_x`` is a :py:class:`~summarizedexperiment.SummarizedExperiment.SummarizedExperiment`.
num_threads:
Number of threads to use in each step.
Expand Down Expand Up @@ -664,13 +676,10 @@ def maybe_filter(x):
raise ValueError("at least one 'use_*' must be True")
if len(embeddings) == 1:
store["combined_pca"] = embeddings[0]
chosen_pcs = store[embeddings[0]].components
else:
embeddings = [store[e].components for e in embeddings]
store["combined_pca"] = scale_by_neighbors(embeddings, **scale_by_neighbors_options, nn_parameters=nn_parameters, num_threads=num_threads)

if isinstance(store["combined_pca"], str):
chosen_pcs = store[store["combined_pca"]].components
else:
chosen_pcs = store["combined_pca"].combined

store["block"] = block
Expand All @@ -682,7 +691,7 @@ def maybe_filter(x):

############ Assorted neighbor-related stuff ⸜(⸝⸝⸝´꒳`⸝⸝⸝)⸝ #############

all_out = run_all_neighbor_steps(
nn_out = run_all_neighbor_steps(
chosen_pcs,
run_umap_options=run_umap_options,
run_tsne_options=run_tsne_options,
Expand All @@ -693,10 +702,10 @@ def maybe_filter(x):
num_threads=num_threads
)

store["tsne"] = all_out.run_tsne
store["umap"] = all_out.run_umap
store["snn_graph"] = all_out.build_snn_graph
store["graph_clusters"] = all_out.cluster_graph
store["tsne"] = nn_out.run_tsne
store["umap"] = nn_out.run_umap
store["snn_graph"] = nn_out.build_snn_graph
store["graph_clusters"] = nn_out.cluster_graph

############ Finding markers (˶˃ ᵕ ˂˶) #############

Expand Down
4 changes: 3 additions & 1 deletion tests/test_analyze.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,9 +69,11 @@ def test_analyze_adt():
res = scranpy.analyze(
sce.assay(0),
adt_x=sce.alternative_experiment("ERCC").assay(0),
suggest_rna_qc_thresholds_options={ "num_mads": 1 }
suggest_rna_qc_thresholds_options={ "num_mads": 1 },
suggest_adt_qc_thresholds_options={ "num_mads": 1 }
)

assert not (res.combined_qc_filter == res.adt_qc_filter).all()
assert not (res.combined_qc_filter == res.rna_qc_filter).all()
assert (res.combined_qc_filter == numpy.logical_and(res.rna_qc_filter, res.adt_qc_filter)).all()

Expand Down

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